LLM2Manim pipeline generates pedagogy-aware Manim animations for STEM, producing slightly better student post-test scores (83% vs 78%), learning gains (d=0.67), and engagement than PowerPoint in a controlled study.
Wireless Networks with Asynchronous Users
4 Pith papers cite this work. Polarity classification is still indexing.
abstract
This paper addresses an interference channel consisting of $\mathbf{n}$ active users sharing $u$ frequency sub-bands. Users are asynchronous meaning there exists a mutual delay between their transmitted codes. A stationary model for interference is considered by assuming the starting point of an interferer's data is uniformly distributed along the codeword of any user. This model is not ergodic, however, we show that the noise plus interference process satisfies an Asymptotic Equipartition Property (AEP) under certain conditions. This enables us to define achievable rates in the conventional Shannon sense. The spectrum is divided to private and common bands. Each user occupies its assigned private band and the common band upon activation. In a scenario where all transmitters are unaware of the number of active users and the channel gains, the optimum spectrum assignment is obtained such that the so-called outage capacity per user is maximized. If $\Pr\{\mathbf{n}>2\}>0$, all users follow a locally Randomized On-Off signaling scheme on the common band where each transmitter quits transmitting its Gaussian signals independently from transmission to transmission. Achievable rates are developed using a conditional version of Entropy Power Inequality (EPI) and an upper bound on the differential entropy of a mixed Gaussian random variable. Thereafter, the activation probability on each transmission slot together with the spectrum assignment are designed resulting in the largest outage capacity.
citation-role summary
citation-polarity summary
verdicts
UNVERDICTED 4representative citing papers
A conceptual framework reframes AI loss of control by anchoring the definition of control to goal setting and alignment, arguing that such loss can occur with existing AI systems.
DBS-Adam, which scales learning rates by batch difficulty from EMA gradient norms and loss, reaches 95.22% accuracy on Bi-LSTM accident severity prediction and shows statistically significant precision gains over AMSGrad, AdamW and AdaBound.
A survey proposing a taxonomy of XAI techniques for food quality research organized by data types and explanation methods.
citing papers explorer
-
Explainable Artificial Intelligence Techniques for Interpretation of Food Models: a Review
A survey proposing a taxonomy of XAI techniques for food quality research organized by data types and explanation methods.